A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED

A regional neural network-based method, “CANYON-MED” is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO−33-), phosphates (PO3−443-) and silicates (SiOH4), and three carbonate system variables: pH on the total scale (pHT), total alkalinity (AT), and dissolved inorganic carbon or total carbon (CT), trained using a specific quality-controlled dataset of reference “bottle” data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0.73 μmol.kg–1 for NO−33-, 0.045 μmol.kg–1 for PO3−443- and 0.70 μmol.kg–1 for Si(OH)4, 0.016 units for pHT, 11 μmol.kg–1 for AT and 10 μmol.kg–1 for CT. A second validation on the ANTARES independent time series confirmed the method’s applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data. Matlab and R code are available at https:// github.com/MarineFou/CANYON-MED/.

Fourrier M., Coppola L., Claustre H., D’Ortenzio F., Sauzède R. & Gattuso J.-P., 2020. A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED. Frontiers in Marine Science 7: 620. doi: 10.3389/fmars.2020.00620. Article.

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